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APPCorp: a corpus for Android privacy policy document structure analysis

  • Shuang Liu
  • , Fan Zhang
  • , Baiyang Zhao
  • , Renjie Guo
  • , Tao Chen
  • , Meishan Zhang*
  • *Corresponding author for this work
  • Tianjin University
  • Alphabet Inc.

Research output: Contribution to journalArticlepeer-review

Abstract

With the increasing popularity of mobile devices and the wide adoption of mobile Apps, an increasing concern of privacy issues is raised. Privacy policy is identified as a proper medium to indicate the legal terms, such as the general data protection regulation (GDPR), and to bind legal agreement between service providers and users. However, privacy policies are usually long and vague for end users to read and understand. It is thus important to be able to automatically analyze the document structures of privacy policies to assist user understanding. In this work we create a manually labelled corpus containing 231 privacy policies (of more than 566,000 words and 7,748 annotated paragraphs). We benchmark our data corpus with 3 document classification models and achieve more than 82% on F1-score.

Original languageEnglish
Article number173320
JournalFrontiers of Computer Science
Volume17
Issue number3
DOIs
StatePublished - Jun 2023
Externally publishedYes

Keywords

  • GDPR
  • document structure analysis
  • graph neural network
  • privacy policy
  • representation learning

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